Big bang in big data

GW150914 was actually a very loud signal - more a big bang than a whimper…

Big bang in big data

For Galileo, it was easy – build a telescope and point it at the sky. You could see the stars and planets with your naked eye and the telescope just made them bigger and let you see much more detail. With gravitational waves, it is different. There is nothing to see, apart from the fleeting effect of them moving two mirrors – apart or together – by an “infinitesimal” distance, inside the most sensitive scientific instrument ever developed. When the project started, the scientists who used the detector did not even know if the thing they were hunting existed – they thought it did, but could not be completely sure until something happened in September 2015 which finally proved it.

For Professor Siong Heng of the Institute for Gravitational Research (IGR), the challenge appears even greater – and more esoteric. He and his colleagues are not gazing up at the sky with a telescope or infrared scanner. They are not looking for an almost imperceptible movement as a ripple in space-time goes by. They are not watching for the signs of an explosion which happened more than one billion years ago far off in space. They are wading through the data generated by LIGO (the Laser Interferometer Gravitational-wave Observatory) to identify the sources of whatever is causing the mirrors to move – looking for a big bang in the midst of big data.

If the detector is the hardware, then Heng and the data analysis group are the software. And as computer power accelerates year after year, and new techniques such as machine learning make data processing quicker and smarter, they play an even more important role in the project – the go-to guys as soon as something happens; which may or may not be gravitational waves. The detector often registers something significant, but the scientists have to be careful not to jump to conclusions. And that is the job of the data analysis group – calming everyone down and then very carefully proving what happened, using the power of maths and statistics.

In addition, says Heng, by creating mathematical models which help to interpret the signals and identify the signatures of different sources of noise (not just from Outer Space but also local noise, including from the detector itself), the data team also help fine-tune the settings and help design future upgrades. There are ”multiple ways to analyse the data,” says Heng, and it's his job to learn from experience so he can do his job better, saving time and making the process much smarter.

Originally from Singapore via Australia, Heng focuses on what is known as “generic transient analysis.” In simple terms, this means identifying statistically-significant correlated signals across multiple gravitational-wave detectors, such as the collision between two black holes – the kind of event which generated the gravitational wave (codenamed GW150914) detected in September 2015 by the LIGO detector – and interpreting the astrophysics behind observed signals. “You see some wiggles in the data from multiple detectors, then check to see if this is consistent with the properties of the detector noise,” says Heng.

As well as being very clever science, however, you could say that it also needs some “educated guesswork” to cut a few corners and process the data as precisely as possible.

For example, when GW150914 arrived, it did not behave 100 per cent as expected. The researchers thought the first detections would be a very small signal buried in a lot of interference or noise, but GW150914 was actually a very loud signal – more a big bang than a whimper. Within three minutes, the data analysis group were already confident the signal was a gravitational wave, but that was just the start of a laborious process to prove it. (Please see A billion years, three minutes later.)

What first identified the signal was something called “generic transient analysis.” If both sets of data from the two detectors (one in Louisiana and the other thousands of miles away in Washington) are correlated at a given time, then the scientists know that they have a good candidate for gravitational waves – not proof but probability. “Matched filtering” techniques then compared the data to a range of predicted waveforms, to provide evidence that the signal originated from the coalescence and merger of two black holes.

On the surface, the methods developed by Heng and his team appear bomb-proof, but ”nature has its ways of surprising us,” and sometimes data analysts may be surprised by deviations from established theory. The predictions may be way off and the data (or sound) can be very different from what is expected. “The data analysis may be correct,” says Heng, “but the instruments may not be doing their job right.” In addition, it takes time to build up a store of real data to help develop better processing methods and fine-tune the system – no data, no tuning. “The data analysis evolves at the same rate as the detector,” says Heng. “They are interdependent.”

Heng also stresses that it’s not a simple case of building models and comparing the data from signals received, then doing a few sums to come up with answers. “We look for correlative excess,” says Heng. In other words, rather than simply filtering data, the challenge is to find the unexpected in the midst of all the data, by using special algorithms to flag up anomalies, rather than examine every single piece of data all the time. Similar methods are now being used in other applications such as medical imaging, including ophthalmology (please see below), as well as food and drink, security and defence.

“In the search for gravitational waves, data analysis is not just a method to check things but also a discovery engine,” says Heng. And the methods used to find gravitational waves in the depths of the Cosmos may also help to show us what is staring us straight in the eye – and even what is hidden deep inside our eyes.

From outer space to inner space

An innovative project which involves collaboration between leading medical technology company Optos and the University of Glasgow’s Institute for Gravitational Research (IGR) promises to help in the detection of retinal defects – using the same kind of Bayesian data analysis methods employed in the search for gravitational waves.

Optos specialises in developing devices which produce high-resolution images of the retina, to provide the information needed for early detection of a wide range of disorders such as retinal detachments and tears, glaucoma, diabetic retinopathy and age-related macular degeneration. The aim is to develop new solutions to automate testing procedures – leading to significant cost savings by improving quality assurance (QA) in the manufacturing process.

Biography

Professor Siong Heng of the Institute for Gravitational Research (IGR) is the chair of the data analysis working group that identified GW150914 as a candidate event – within just a few minutes of the gravitational waves reaching the Earth. Heng works principally on “burst detection and astrophysics,” seeking to identify sources while making minimal assumptions about the precise mathematical form of their waveform, so we are in a position to detect the unexpected and not just sources we’re already confident exist. Heng’s interests also extend into developing powerful algorithms – using methods such as machine learning – to perform automated image processing and characterisation in diverse fields beyond astrophysics.